Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning : Bim 2023

• Autore: Arefin Mohammad Shamsul
• Pubblicazione: Singapore : , : Springer Singapore Pte. Limited, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (1053 pages)
• Altri autori: KaiserM. Shamim BhuiyanTouhid DeyNilanjan MahmudMufti
• Nota di contenuto: Intro -- Organization -- Preface -- Contents -- Editors and Contributors -- Informatics for Emerging Applications -- A Deep Learning Approach to Predict Cryptocurrency Price by Evaluating Sentiment and Stock Market Correlations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Proposed System -- 3.2 Data Preprocessing -- 3.3 Model for Cryptocurrency Data -- 3.4 Model for Sentiment Analysis -- 4 Evaluation -- 4.1 Dataset Description -- 4.2 Experimentation and Result Analysis -- 5 Conclusion -- References -- Dominance by Stability: A Framework for Top k Dominating Query on Incomplete Data -- 1 Introduction -- 2 Related Works -- 3 Top-k Dominating Query by Stability (TKDS) -- 3.1 Bucketing -- 3.2 Dominating Score Computation -- 3.3 Bucket Implementation -- 3.4 Top-k Query Processing -- 4 Performance Evaluation -- 4.1 Dataset -- 4.2 Result Analysis -- 5 Conclusion -- References -- Phylogeny Reconstruction Using k-mer Derived Transition Features -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 k-mer Length, Distribution Vector, and Position List Generation -- 3.2 Standard Deviation, Median, and Transition Spatial Features -- 3.3 Phylogenetic Distance and Tree Reconstruction -- 4 Experimental Results -- 4.1 Datasets and Configurations -- 4.2 Soundness of k-mer Length l and Scalability of the Method -- 4.3 Benchmark Test Performance -- 4.4 Performance with Respect to State-of-the-Art Methods -- 4.5 Discussion -- 5 Conclusion -- References -- Developing an Interpretable Machine Learning Model for Divorce Prediction -- 1 Introduction -- 2 Related Works -- 3 Understandable AI Model for Divorce Prediction -- 3.1 Proposed Methodology -- 3.2 Evaluation Metrics -- 3.3 Dataset Description -- 4 Result and Discussion -- 4.1 Performance Analysis of Individual Classifier -- 4.2 SHAP Value Analysis -- 5 Conclusion and Future Work. ; References -- Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Dataset Description and Preprocessing -- 4 Methodology -- 4.1 Model Architecture -- 4.2 Training YOLOv8 -- 5 Result and Discussions -- 6 Implementation and Future Work -- 7 Conclusion -- References -- An Explainable AI Enable Approach to Reveal Feature Influences on Social Media Customer Purchase Decisions -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Overview of the Proposed Methodology -- 3.2 Description of the Dataset -- 3.3 Techniques for Dataset Preprocessing -- 3.4 ML Algorithms for Analysis -- 3.5 Performance Measure Metrics -- 3.6 Details of XAI Tools -- 4 Result and Analysis -- 4.1 Performance of the ML Algorithms to Predict Social Media Customer Purchase Decision -- 4.2 Explainability of RF by the XAI Tools -- 5 Conclusion and Future Works -- References -- Field Programmable Gate Array in DNA Computing -- 1 Introduction -- 2 Background -- 2.1 DNA Computing -- 2.2 DNA Basic Operations -- 3 FPGA Logic Block -- 3.1 Architecture of Basic Components -- 3.2 Working Procedure -- 4 FPGA Logic Block Algorithm -- 5 Conclusion -- References -- XAI-Driven Model Explainability and Prediction of P2P Bank Loan Default Network -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Overview of Proposed Methodology -- 3.2 Description of the Dataset -- 3.3 Feature Selection Procedure -- 3.4 Data Balancing in Highly Imbalanced Dataset -- 3.5 Description of the ML Algorithms for Prediction -- 3.6 Performance Measure Techniques -- 3.7 Description of the Explainable AI Tools -- 4 Result and Analysis -- 5 Conclusion and Future Works -- References -- Design Implication of a Compact-Sized, Low-Fidelity Rover for Tough Terrain Exploration -- 1 Introduction -- 2 Comparative Study. ; 3 Foundational Concepts and Technologies -- 3.1 Embedded System and Robotics -- 3.2 Navigation System in Miniature Robots -- 3.3 Low-Fidelity Robot -- 3.4 Mini Rover -- 4 Systematic Approach -- 4.1 Task Outline -- 5 Implementation -- 5.1 System Design -- 5.2 Mathematical Calculation -- 6 Discussions and Analysis -- 7 Conclusion -- References -- VioNet: An Enhanced Violence Detection Approach for Videos Using a Fusion Model of Vision Transformer with Bi-LSTM and 3D Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Result and Discussion -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Performance Evaluation -- 4.4 Comparison with Other Methods -- 5 Conclusion -- References -- Rank Your Summaries: Enhancing Bengali Text Summarization Via Ranking-Based Approach -- 1 Introduction -- 2 Bengali Summary Ranker -- 2.1 Proposed Approach -- 2.2 Models -- 3 Evaluation -- 3.1 Datasets -- 3.2 Hyper-Parameter Settings -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Result Analysis -- 4.1 Quantitative Analysis -- 4.2 Qualitative Analysis -- 5 Related Works -- 6 Conclusion -- References -- An Efficient Machine Learning Classification Model for Rainfall Prediction in Bangladesh -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Predicted Models -- 3.2 Models Setting and Analysis Steps -- 3.3 Flow Diagram of This Study -- 3.4 Experiment Dataset -- 3.5 Data Preprocess -- 4 Results and Discussion -- 4.1 Actual and Predicted Results -- 4.2 Models Performance Table -- 4.3 Graphical Representation -- 5 Conclusions and Future Work -- References -- Study on the Analysis and Prediction of Drug Addiction Among University Students of Bangladesh Using Machine Learning -- 1 Introduction -- 1.1 Data Collection -- 1.2 Assuring the Quality -- 1.3 Choosing an Algorithm -- 1.4 Limitations -- 1.5 Ethical Consideration. ; 2 Literature Review -- 3 Background Study -- 3.1 K-Nearest Neighbor -- 3.2 Logistic Regression -- 3.3 Gaussian Naïve Bayes -- 3.4 Support Vector Machine -- 3.5 Random Forest -- 3.6 Neural Network (Multilayer Perceptron) -- 4 Methodology -- 4.1 Data Assemblage and Dataset -- 4.2 Visualization -- 4.3 Algorithm Analysis -- 5 Experiment Results -- 6 Conclusion and Future Work -- References -- Artificial Intelligence for Imaging Applications -- A Deep CNN-Based Approach for Revolutionizing Bengali Handwritten Numeral Recognition -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset Acquisition and Description -- 3.2 Data Augmentation -- 3.3 Convolutional Neural Network -- 3.4 Proposed Architecture -- 4 Experiment and Result Analysis -- 4.1 Data Preprocessing -- 4.2 Experimental Settings -- 4.3 Result Analysis -- 4.4 Evaluating Different Route Configurations -- 4.5 Comparison with Prior Works -- 5 Conclusion -- References -- Performance Analysis of Multiple Deep Learning Models for Image Retrieval Problems -- 1 Introduction -- 2 Related Work -- 2.1 Literature Review -- 2.2 Deep Learning Methods -- 3 Research Methodology -- 3.1 Image Acquisition -- 3.2 Model Adaptation -- 3.3 Implementation and Training -- 4 Experimental Result and Analysis -- 5 Conclusion -- References -- Advancing Lung Cancer Diagnosis Through Deep Learning and Grad-CAM-Based Visualization Techniques -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methodology -- 3.1 Dataset Description -- 3.2 Data Preprocessing -- 3.3 Proposed Workflow -- 3.4 Model Architecture -- 3.5 Grad-CAM Visualization -- 4 Result Analysis -- 4.1 Method Evaluation Metrics -- 4.2 Comparison with Pre-Trained Other Models -- 4.3 Comparison with Related Works -- 4.4 Obtained Result -- 5 Discussion -- 6 Conclusion -- References. ; A Novel Approach to Detect Stroke from 2D Images Using Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Data Sets Characteristics -- 4 Proposed Methodology -- 5 Result and Discussion -- 5.1 Batch Size -- 5.2 Impact of Learning Rate -- 5.3 Adam Optimizer -- 5.4 Kernel Size -- 5.5 Comparison with Current Studies -- 6 Conclusion and Future Work -- References -- Enhancing Pneumonia Diagnosis: An Ensemble of Deep CNN Architectures for Accurate Chest X-Ray Image Analysis -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 4 Dataset -- 5 Image Pre-processing -- 5.1 Resizing -- 5.2 Augmentation -- 5.3 Normalization -- 6 Deep CNN Model Architectures Using Transfer Learning -- 6.1 Convolutional Neural Network Model Architectures -- 6.2 Transfer Learning: Fine Tuning -- 7 Ensemble Learning -- 8 Results and Discussion -- 8.1 Output of Single Model -- 8.2 Output of Ensemble Model -- 9 Conclusion -- References -- Dataset for Road Roughness Assessment Using Image Classification Techniques and Deep Learning Models: A Case Study on Bangladeshi National Highways -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Dataset Comparison -- 3.4 Model Training -- 3.5 Feature Map Extraction -- 3.6 Analysis -- 4 Conclusion -- References -- Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Distance Local Ternary Pattern EAdLTP for Image Classification -- 1 Introduction -- 2 Background Study -- 2.1 Local Binary Pattern LBP -- 2.2 Local Ternary Pattern LTP -- 3 Noise-Aware-Based Evaluation Window-Based Adjacent Distance Local Ternary Pattern EAdLTP -- 3.1 Encoding the Value of xp -- 3.2 Calculating the Value of Adjacent Distance Local Ternary Pattern EAdLTP -- 4 Experiment Analysis -- 5 Conclusion -- References -- Sentiment Analysis from YouTube Video Using Bi-LSTM-GRU Classification. ; 1 Introduction.
• Titolo autorizzato: Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning
• ISBN: 981-9989-37-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910847080403321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems Series